We propose to extend the current focus of BCI-based stroke rehabilitation beyond

sensorimotor-rhythms to also include associative brain areas. In particular, we argue that neurofeedback training of brain rhythms that signal a state-of-mind beneficial for motorlearning is likely to enhance post-stroke motor rehabilitation. We propose an adaptive neurofeedback paradigm for this purpose and demonstrate its viability on EEG data recorded with five healthy subjects

Cetin, Mujdat

Grosse-Wentrup, Moritz

Meyer, Timm

Ozdenizci, Ozan

Çetin, Müjdat

Özdenizci, Ozan

Sabanci University Research Database

Towards Neurofeedback Training of AssociativeBrain Areas for Stroke RehabilitationOzan O¨zdenizci1, Timm Meyer2, Mu¨jdat C¸etin1 and Moritz Grosse-Wentrup21 Sabancı University, Faculty of Engineering and Natural Sciences, Istanbul, Turkeyoozdenizci@sabanciuniv.edu, mcetin@sabanciuniv.edu2 Max Planck Institute for Intelligent Systems, Dept. Empirical Inference, Tu¨bingen, Germanytmeyer@tuebingen.mpg.de, moritzgw@tuebingen.mpg.deAbstractWe propose to extend the current focus of BCI-based stroke rehabilitation beyondsensorimotor-rhythms to also include associative brain areas. In particular, we argue thatneurofeedback training of brain rhythms that signal a state-of-mind beneficial for motor-learning is likely to enhance post-stroke motor rehabilitation. We propose an adaptive neu-rofeedback paradigm for this purpose and demonstrate its viability on EEG data recordedwith five healthy subjects.1 IntroductionWhile initially conceived as communication devices for severely paralyzed patients, brain-computer interfaces (BCIs) have recently been considered in the context of post-stroke motorrehabilitation [1]. Here, BCIs are employed to synchronize movement intent, as decoded by aBCI from sensori-motor rhythms, with congruent haptic feedback, e.g., as delivered by an ex-oskeleton [2]. This artificial re-establishment of the sensorimotor feedback loop has been shownto support modulation of sensorimotor-rhythms [3] and result in enhanced post-stroke recovery[4]. In this paper, we argue that, motivated by the impressive results achieved to date, thecurrent focus should be extended beyond sensori-motor- to also include associative brain areas.This argument is based on empirical evidence that a variety of brain rhythms beyond those inprimary sensorimotor areas are related to the extent of post-stroke impairment. For instance,the global ratio of δ- to α-power of the brain’s electromagnetic field has been found to correlatewith the extent of post-stroke disability and predict subsequent recovery [5]. We interpret suchabnormal activation patterns as disturbances of the balance of large scale cortical networks [6],and argue that re-establishing their natural balance by means of BCI-based neurofeedback islikely to support the brain in post-stroke recovery.In order to turn this hypothesis into a viable stroke rehabilitation protocol, several interre-lated problems need to be addressed. Firstly, we need to identify which large scale networksare involved in sensori-motor learning in healthy control subjects. Next, we have to investi-gate how activation patterns of and between these networks are disturbed in stroke patients,and elucidate how these disturbances relate to post-stroke recovery. And thirdly, we need totrain patients via neurofeedback to establish activation patterns of large scale networks thatare associated with good post-stroke recovery.In the present work, we address the viability of the last issue. In a recent publication, we haveidentified EEG correlates of motor-learning performance [7]. In particular, we have providedevidence that parieto-occipital α-power during rest as well as during movement preparationpredicts performance in a subsequent reaching movement. This led us to hypothesize thatparieto-occipital α-power reflects activity in a cortical network that is tuned by the brain tooptimize motor-learning performance. Here, we propose an adaptive neurofeedback training1scheme to modulate parieto-occipital α-power in a way that we predict will enhance motor-learning performance. Based on experimental data from five healthy subjects we argue that,firstly, subjects are able to modulate parieto-occipital α-power by means of neurofeedback,and, secondly, that this modulation is not task-specific but extends into the inter-trial resting-periods. Taken together, these results indicate the feasibility of using neurofeedback to supportsubjects in generating brain activation patterns that are associated with good motor-learningperformance. This constitutes an important building block towards a stroke therapy based onneurofeedback training of associative brain areas.2 Methods2.1 Experimental DataFive healthy subjects were recruited for this study, each of which completed two training ses-sions. Each session lasted one hour with one week interval between sessions. Prior to thetraining sessions all participants gave their informed consent after the training procedure wasexplained to them in accordance with guidelines set by the Max Planck Society. During thetraining sessions, a 120-channel EEG was recorded at 1 kHz sampling rate, using active EEGelectrodes and a QuickAmp amplifier (BrainProducts, Gilching, Germany). Electrodes wereplaced according to the 10-20 system, with Cz as the initial reference electrode. All data wasre-referenced to common average reference.2.2 Adaptive Online FeedbackEach training session included one resting-state baseline and eight training blocks with oneminute breaks in between. For the resting-state baseline, subjects were instructed to relaxfor five minutes with eyes open, looking at a fixation cross displayed centrally on a screenapproximately 1.5 m in front of the subjects. This resting-state data was used to calibrate thefeedback for the subsequent training session. Specifically, the 120-dimensional raw EEG datax[t] was first spatially filtered by a filter w to obtain a one-dimensional signal y[t] = wTx[t].The spatial filter w was taken from a group-wise independent component analysis (ICA) of aprevious study [7], in which the corresponding independent component’s α-power was found topredict performance in a motor-learning task. Figure 1a displays the source localization resultsobtained for this independent component in parieto-occipital areas (adapted from [7]). Next,log-bandpower of the spatially filtered signal was computed in the α-band (8-14 Hz), using aFFT in conjunction with a Hanning window, in a sliding window of 2 s with a window stepsize of 100 ms. This signal processing pipeline was used to compute the mean and standard(a) (b)Figure 1: a) Parieto-occipital region used for neurofeedback [7]. b) Visual stimulus.deviation of resting-state α-power.In each training block, the same signal processing pipeline was used to provide subjects withvisual feedback on parieto-occipital α-power. The current estimate of parieto-occipital α-powerwas displayed as the vertical height of a rectangular visual stimulus on the screen (Figure 1b),which was updated at 25 Hz. The bottom of the rectangle corresponded to the mean log-bandpower of the resting-state baseline. One training block consisted of 15 trials. In each trial,subjects were given the objective to increase the vertical height of the rectangle to reach anadaptively determined target height level marked by an upper bar on the screen. Subjects hadto learn to up-regulate the presented brain activity with eyes open and keep the activity atthat level or above for a cumulative time of two seconds. No instructions were given to thesubjects on how to achieve this goal. If subjects succeeded in this task in a 15 second trial,the rectangle turned green. Otherwise, the next trial began after a short resting-period, with arandomly determined length between 4.5 to 5.5 seconds. For the first training block, the targetdistance was set to one standard deviation of the log-bandpowers in the resting-state baseline.Depending on the performance of the subject, the target distance changed. It increased by 0.2standard deviations of the resting-state baseline phase in the next block, if the success rate ofthe previous block over 15 trials was higher than 70%. The target distance decreased by thesame amount if the success rate was lower than 60%. This adaptive approach was implementedto balance any potential frustration or negligence of the subject on the task [8]. During thetraining sessions, presentation of the stimulus and real-time data processing was performedwith the BCI2000 software [9] and its extension BCPy2000 [10].2.3 Offline Data AnalysisFollowing the last training session, the data of all subjects and sessions was visually inspectedfor contamination by ocular artifacts. The data of one session of one subject was discarded,as it was heavily contaminated by eye blinks. To assess the overall effect of neurofeedbacktraining, we then pooled the data of all subjects and sessions. Specifically, we computed thecorrelation of trial-number, ranging from one to 120, with parieto-occipital α-power, averagedacross subjects and sessions, in each trial. In this way, we estimated the linear trend in parieto-occipital α-power across a training session. We also computed this correlation for α-power ateach individual recording channel in order to investigate the topography of α-power modulation.Finally, we computed the same correlation only using the EEG data of the resting-periods priorto each trial.(a) (b) (c)Figure 2: a) Grand average of trial α-log-bandpower values (red is the linear fit). b) Topographyof grand average log-bandpower correlations with trial numbers within a session of 120 trials inα-band (for trial phases). c) Same topography in (b) obtained for rest phases.3 ResultsParieto-occipital α-power displays a positively-sloped linear trend within a training session of120 trials (Figure 2a) with a correlation coefficient of ρ = 0.26. A permutation test on thetemporal order of trials with 104 permutations rejected the null-hypothesis of zero correlationwith p = 0.002 (N = 120). Correlation values between α-power and trial number at eachindividual electrode are shown in Figure 2b and 2c for trial- and pre-trial data, respectively.These results indicate that subjects learned to modulate parietal α-power and that this self-regulation extended beyond the actual feedback phase into the inter-trial resting phases.4 DiscussionIn this pilot study, we presented an adaptive neurofeedback training paradigm that enables sub-jects to up-regulate parieto-occipital α-power. Importantly, we could provide evidence that up-regulation of α-power was sustained in the inter-trial periods, indicating that subjects learnedto induce a stable state-of-mind that we predict will be beneficial for motor-learning [7]. Infuture work, this training paradigm will be carried out by stroke patients prior to a rehabilita-tion session. We hypothesize that this will be beneficial for motor-learning and hence supportpost-stroke motor recovery.References[1] M. Grosse-Wentrup, D. Mattia, and K. Oweiss. Using brain–computer interfaces to induce neuralplasticity and restore function. Journal of Neural Engineering, 8(2):025004, 2011.[2] M. Gomez-Rodriguez, M. Grosse-Wentrup, J. Hill, A. Gharabaghi, B. Scho¨lkopf, and J. Peters.Towards brain–robot interfaces in stroke rehabilitation. In IEEE International Conference onRehabilitation Robotics (ICORR), pages 1–6. IEEE, 2011.[3] M. Gomez-Rodriguez, J. Peters, J. Hill, B. Scho¨lkopf, A. Gharabaghi, and M. Grosse-Wentrup.Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery. Journal ofNeural Engineering, 8(3):036005, 2011.[4] A. Ramos-Murguialday et al. Brain–machine interface in chronic stroke rehabilitation: A controlledstudy. Annals of Neurology, 74(1):100–108, 2013.[5] S. P. Finnigan, M. Walsh, S. E. Rose, and J. B. Chalk. Quantitative EEG indices of sub-acuteischaemic stroke correlate with clinical outcomes. Clinical Neurophysiology, 118(11):2525–2532,2007.[6] S. L. Bressler and V. Menon. Large-scale brain networks in cognition: emerging methods andprinciples. Trends in Cognitive Sciences, 14(6):277–290, 2010.[7] T. Meyer, J. Peters, T. O. Zander, B. Scho¨lkopf, and M. Grosse-Wentrup. Predicting motorlearning performance from electroencephalographic data. Journal of NeuroEngineering and Reha-bilitation, 11:24, 2014.[8] S. Othmer, S. F. Othmer, and D. A. Kaiser. EEG biofeedback: An emerging model for itsglobal efficacy. In J. Evans and A. Abarbanel, editors, Introduction to Quantitative EEG andNeurofeedback, pages 243–310. Academic Press, New York, 1999.[9] G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer, and J. R. Wolpaw. BCI2000: Ageneral-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engi-neering, 51(6):1034–1043, 2004.[10] BCPy2000, http://bci2000.org/downloads/BCPy2000/.

Towards neurofeedback training of associative brain areas for stroke rehabilitation

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Towards neurofeedback training of associative brain areas for stroke rehabilitation

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